


'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any]				)	->						Dict:
     __lowerCamelCase  :      Any														=						[
         'encoder.version',
         'decoder.version',
         'model.encoder.version',
         'model.decoder.version',
         'decoder.output_projection.weight',
         '_float_tensor',
         'encoder.embed_positions._float_tensor',
         'decoder.embed_positions._float_tensor',
     ]
     for k in ignore_keys:
          state_dict.pop(UpperCAmelCase_ ,       UpperCAmelCase_				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Tuple				)	->						Optional[Any]:
     __lowerCamelCase  :      Optional[Any]														=						list(s_dict.keys()				)
     for key in keys:
          if "transformer_layers" in key:
               __lowerCamelCase  :      List[str]														=						s_dict.pop(UpperCAmelCase_				)
          elif "subsample" in key:
               __lowerCamelCase  :      Optional[int]														=						s_dict.pop(UpperCAmelCase_				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any]				)	->						int:
     __lowerCamelCase							,			__lowerCamelCase  :      List[str]														=						emb.weight.shape
     __lowerCamelCase  :      List[str]														=						nn.Linear(UpperCAmelCase_ ,       UpperCAmelCase_ ,       bias=UpperCAmelCase_				)
     __lowerCamelCase  :      int														=						emb.weight.data
     return lin_layer



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							Any				)	->						List[Any]:
     __lowerCamelCase  :      Tuple														=						torch.load(UpperCAmelCase_ ,       map_location='cpu'				)
     __lowerCamelCase  :      Any														=						mam_aaa['args']
     __lowerCamelCase  :      Any														=						mam_aaa['model']
     __lowerCamelCase  :      Optional[Any]														=						state_dict['decoder.output_projection.weight']

     remove_ignore_keys_(UpperCAmelCase_				)
     rename_keys(UpperCAmelCase_				)

     __lowerCamelCase  :      Tuple														=						state_dict['decoder.embed_tokens.weight'].shape[0]

     __lowerCamelCase  :      Optional[int]														=						args.share_decoder_input_output_embed

     __lowerCamelCase  :      Optional[int]														=						[int(UpperCAmelCase_				) for i in args.conv_kernel_sizes.split(','				)]
     __lowerCamelCase  :      int														=						SpeechaTextConfig(
         vocab_size=UpperCAmelCase_ ,       max_source_positions=args.max_source_positions ,       max_target_positions=args.max_target_positions ,       encoder_layers=args.encoder_layers ,       decoder_layers=args.decoder_layers ,       encoder_attention_heads=args.encoder_attention_heads ,       decoder_attention_heads=args.decoder_attention_heads ,       encoder_ffn_dim=args.encoder_ffn_embed_dim ,       decoder_ffn_dim=args.decoder_ffn_embed_dim ,       d_model=args.encoder_embed_dim ,       dropout=args.dropout ,       attention_dropout=args.attention_dropout ,       activation_dropout=args.activation_dropout ,       activation_function='relu' ,       num_conv_layers=len(UpperCAmelCase_				) ,       conv_channels=args.conv_channels ,       conv_kernel_sizes=UpperCAmelCase_ ,       input_feat_per_channel=args.input_feat_per_channel ,       input_channels=args.input_channels ,       tie_word_embeddings=UpperCAmelCase_ ,       num_beams=5 ,       max_length=2_00 ,       use_cache=UpperCAmelCase_ ,       decoder_start_token_id=2 ,       early_stopping=UpperCAmelCase_ ,       )

     __lowerCamelCase  :      str														=						SpeechaTextForConditionalGeneration(UpperCAmelCase_				)
     __lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						model.model.load_state_dict(UpperCAmelCase_ ,       strict=UpperCAmelCase_				)
     if len(UpperCAmelCase_				) > 0 and not set(UpperCAmelCase_				) <= {
         "encoder.embed_positions.weights",
         "decoder.embed_positions.weights",
     }:
          raise ValueError(
              'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
              F' but all the following weights are missing {missing}'				)

     if tie_embeds:
          __lowerCamelCase  :      List[Any]														=						make_linear_from_emb(model.model.decoder.embed_tokens				)
     else:
          __lowerCamelCase  :      int														=						lm_head_weights

     model.save_pretrained(UpperCAmelCase_				)


if __name__ == "__main__":
      A__						:   Any				=    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("""--fairseq_path""", type=str, help="""Path to the fairseq model (.pt) file.""")
      parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      A__						:   List[str]				=    parser.parse_args()
      convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_SCREAMING_SNAKE_CASE 		=      """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
_SCREAMING_SNAKE_CASE 		=      [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
_SCREAMING_SNAKE_CASE 		=      dict(zip(vocab, range(len(vocab))))
_SCREAMING_SNAKE_CASE 		=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     _SCREAMING_SNAKE_CASE 		=      Path(tmpdirname)
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     _SCREAMING_SNAKE_CASE 		=      FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

_SCREAMING_SNAKE_CASE 		=      FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_SCREAMING_SNAKE_CASE 		=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
